Vector Search Explained: Semantic Similarity Retrieval

AI Impact Summary

Vector search represents a significant shift in information retrieval by leveraging vector embeddings to capture semantic meaning, enabling similarity searches across large datasets without relying on exact keyword matches. This approach is particularly relevant for AI-native applications struggling with traditional search limitations. The implementation details, including OpenAI embeddings, cosine similarity, and Approximate Nearest Neighbor (ANN) algorithms within vector databases, highlight the technical complexity and performance optimizations driving this technology.